摘要
为解决在复杂噪声和工频及其倍频干扰条件下滚动轴承故障诊断问题,提高诊断准确率,进行了经验模态分解(EMD)和支持向量机(SVM)的研究,给出了相应的决策流程。基于改进的EMD分解的特征提取算法,选取故障特征明显的IMF分量进行特征提取,最大限度地滤除了低频噪声干扰,捕捉到信号的故障特征,然后将特征集输入到SVM分类器中进行识别,结果表明:该方法对于轴承故障识别具有较高的准确率,为确保轴承安全运行和快速故障诊断提供了理论支持。
In order to solve the problem of rolling bearing fault diagnosis under the complex noise, power frequency and its har-monics interference conditions, to improve the accuracy of diagnosis, the empirical mode decomposition （EMD） and study of the sup-port vector machine （SVM） were carried out, then the corresponding decision-making process was given. The feature extraction algo-rithm was applied based on improved EMD decomposition, and the fault features with obvious intrinsic mode functions （IMF） compo-nent were selected for feature extraction. Maximum filter in extend was done on low frequency noise interference, the fault features of the signal were captured, and then the feature sets were input to the SVM classifier to identify. The results show that the method for bearing fault identification has higher accurate rate, which provides a theoretical support for ensure the safe operation of the bearing and fast fault diagnosis.
作者
付大鹏
翟勇
于青民
FU Dapeng ZHAI Yong YU Qingmin(School of Mechanical Engineering, Northeast Dianli University, Jilin Jilin 132012, China School of Control Science and Engineering, Shandong University, Jinan Shandong 250000, China)
出处
《机床与液压》
北大核心
2017年第11期184-187,共4页
Machine Tool & Hydraulics
